Geodata Requirements for Mapping Protective Functions and Effects of Forests

Mapping of protective functions and effects of forests is subject to geodata on 1) natural hazard susceptibilities (hazard potential), 2) assets to be protected (damage potential), and 3) forest conditions, that is, forest use (legal extent) and cover (structure). Objectives in terms of legal definitions of assets and levels of risk acceptance (protection targets) as well as on the necessary and guaranteed reliability of the map products determine the mapping scale and the requirements for the methods and input data to be used. However, applied definitions of protection targets are often missing in the legislative bases and mapping approaches must rather be adapted to the existing geodata, their conceptual data model and quality, than simply using existing methods. Agreeing on the assets to be protected and the quality of their digital representation in terms of spatial resolution, positional accuracy, currentness, topological consistency, and entities is crucial for mapping object protective forests. The reliability of assessing protective effects of forests for large areas based on information acquired with remote sensing techniques depends on the temporal match, spatial and spectral resolutions, and limitations in representing current forest conditions by spectral and elevation data

Development of Harmonized Indicators and Estimation Procedures for Forests with Protective Functions against Natural Hazards in the Alpine Space

The present study was developed in the context of Regulation (EC) 2152/2003 on the monitoring of forest and environmental interactions, the so-called "Forest Focus" Regulation. The specific objective of this study was to explore the possible contribution of the national forest inventories (NFIs) to assess protective functions of for-ests in the alpine space. Key components of protective functions could be determined with the help of on-going national and international studies and processes. In order to grant consistency, definitions of forest area, dam-age potential and hazard potential had to be harmonised. Based on those, a strategy for monitoring and report-ing aspects of protective functions of mountain forests in the alpine space was proposed. Estimation procedures based on existing NFI data and field assessments and their integration in different remote sensing techniques were tested for harmonised monitoring. Final results are presented in this report.JRC.DDG.H.7-Land management and natural hazard

Protective Effects of Forests against Gravitational Natural Hazards

In this chapter, we give a short overview of the protective effects of forests against snow avalanches, landslides and rockfall hazards in mountain areas. The overview is based on the protective mechanisms provided by forest and connects them to the effect-related indicators of forest structure from literature and European protective forest management guidelines. The thresholds of the effect-related indicators are hazard-related silvicultural targets for forest management and critical values for hazard risk assessment. The assessment of the protective effects of forests is a central part of natural hazard risk analysis and requires information on different spatial levels from single tree to slope-scale attributes. Forests are efficient in preventing snow avalanche and landslide initiation; however, they are usually unable to stop large masses of snow, soil and rock in motion. Therefore, guidelines on silvicultural targets and practices must focus on the mitigation of hazard onset probabilities at the stand-scale; however, existing guidelines under- or overestimate the protective effects of forests. Effects of forests on hazard propagation are difficult to implement in forest and risk management practice. Hence, the European protective forest management guidelines do not contain any or only general specifications that simplify the determining factors and their relationships

Protective Forests for Ecosystem-based Disaster Risk Reduction (Eco-DRR) in the Alpine Space

Mountain forests are an efficient Forest-based Solution (FbS) for Ecosystem-based Disaster Risk Reduction (Eco-DRR) by lowering the frequency, magnitude, and/or intensity of natural hazards. Technical protection measures are often poor solutions as stand-alone measures to reduce disaster risk limited by material wear and fatigue or financial resources and aesthetical values. Protective forests should therefore be considered as key elements in integrated risk management strategies. However, the definition of protective forests and the understanding and assessment of their protective functions and effects differ greatly among Alpine Space countries. In this chapter, we present a short introduction to the concept of Eco-DRR and companion terms and propose a definition of FbS as a specific case of Nature-based Solutions for an ecosystem-based and integrated risk management of natural hazards. That is, we guide the reader through the maze of existing definitions and concepts and try to disentangle their meanings. Furthermore, we present an introduction to forest regulations in the Alpine Space and European protective forest management guidelines. Our considerations and recommendations can help strengthen the role of protective forests as FbS in Eco-DRR and the acknowledgment of the key protective function they have and the crucial protective effects they provide in mountain areas

Assessing the protective role of alpine forests against rockfall at regional scale

Worldwide, mountain forests represent a significant factor in reducing rockfall risk over long periods of time on large potential disposition areas. While the economic value of technical protection measures against rockfall can be clearly determined and their benefits indicated, there is no general consensus on the quantification of the protective effect of forests. Experience shows that wherever there is forest, the implementation of technical measures to reduce risk of rockfall might often be dispensable or cheaper, and large deforestations (e.g. after windthrows, forest fires, clear-cuts) often show an increased incidence of rockfall events. This study focussed on how the protective effect of a forest against rockfall can be quantified on an alpine transregional scale. We therefore estimated the runout length, in terms of the angle of reach, of 700 individual rockfall trajectories from 39 release areas from Austria, Germany, Italy and Slovenia. All recorded rockfall events passed through forests which were classified either as coppice forests or, according to the CORINE classification of land cover, as mixed, coniferous or broadleaved dominated high forest stands. For each individual rockfall trajectory, we measured the forest structural parameters stem number, basal area, top height, ratio of shrub to high forest and share of coniferous trees. To quantify the protective effect of forests on rockfall, a hazard reduction factor is introduced, defined as the ratio between an expected angle of reach without forest and the back-calculated forest-influenced angles of reach. The results show that forests significantly reduce the runout length of rockfall. The highest reduction was observed for mixed high forest stands, while the lowest hazard reduction was observed for high forest stands dominated either by coniferous or broadleaved tree species. This implies that as soon as one tree species dominates, the risk reduction factor becomes lower. Coppice forests showed the lowest variability in hazard reduction. Hazard reduction due to forests increases, on average, by 7% for an increase in the stem number by 100 stems per hectare. The proposed concept allows a global view of the effectiveness of protective forests against rockfall processes and thus enable to value forest ecosystem services for future transregional assessments on a European level. Based on our results, general cost%benefit considerations of nature-based solutions against rockfall, such as protective forests as well as first-order evaluations of rockfall hazard reduction effects of silvicultural measures within the different forest types, can be supported

Protective Forests for Ecosystem-based Disaster Risk Reduction (Eco-DRR) in the Alpine Space

Mountain forests are an efficient Forest-based Solution (FbS) for Ecosystem-based Disaster Risk Reduction (Eco-DRR) by lowering the frequency, magnitude, and/or intensity of natural hazards. Technical protection measures are often poor solutions as stand-alone measures to reduce disaster risk limited by material wear and fatigue or financial resources and aesthetical values. Protective forests should therefore be considered as key elements in integrated risk management strategies. However, the definition of protective forests and the understanding and assessment of their protective functions and effects differ greatly among Alpine Space countries. In this chapter, we present a short introduction to the concept of Eco-DRR and companion terms and propose a definition of FbS as a specific case of Nature-based Solutions for an ecosystem-based and integrated risk management of natural hazards. That is, we guide the reader through the maze of existing definitions and concepts and try to disentangle their meanings. Furthermore, we present an introduction to forest regulations in the Alpine Space and European protective forest management guidelines. Our considerations and recommendations can help strengthen the role of protective forests as FbS in Eco-DRR and the acknowledgment of the key protective function they have and the crucial protective effects they provide in mountain areas.</jats:p

Sensitivity analysis and calibration of a dynamic physically-based slope stability model

Abstract. Physically-based modelling of slope stability at catchment scale is still a challenging task. Applying a physically-based model at such scale (1 : 10,000 to 1 : 50,000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) if parameters cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically-based slope stability models. In the present study a dynamic physically-based coupled hydrological/geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multi-temporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, effective angle of internal friction, effective cohesion) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.5 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak runoff increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity. </jats:p

Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows

Abstract. Models and simulation tools for gravitational mass flows (GMFs) such as snow avalanches, rockfall, landslides, and debris flows are important for research, education, and practice. In addition to basic simulations and classic applications (e.g., hazard zone mapping), the importance and adaptability of GMF simulation tools for new and advanced applications (e.g., automatic classification of terrain susceptible for GMF initiation or identification of forests with a protective function) are currently driving model developments. In principle, two types of modeling approaches exist: process-based physically motivated and data-based empirically motivated models. The choice for one or the other modeling approach depends on the addressed question, the availability of input data, the required accuracy of the simulation output, and the applied spatial scale. Here we present the computationally inexpensive open-source GMF simulation tool Flow-Py. Flow-Py's model equations are implemented via the Python computer language and based on geometrical relations motivated by the classical data-based runout angle concepts and path routing in three-dimensional terrain. That is, Flow-Py employs a data-based modeling approach to identify process areas and corresponding intensities of GMFs by combining models for routing and stopping, which depend on local terrain and prior movement. The only required input data are a digital elevation model, the positions of starting zones, and a minimum of four model parameters. In addition to the major advantage that the open-source code is freely available for further model development, we illustrate and discuss Flow-Py's key advancements and simulation performance by means of three computational experiments. Implementation and validation. We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topographies. Performance. Flow-Py's performance and low computation time are demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. Modularity and expandability. The modular and adaptive Flow-Py development environment allows access to spatial information easily and consistently, which enables, e.g., back-tracking of GMF paths that interact with obstacles to their starting zones. The aim of this contribution is to enable the reader to reproduce and understand the basic concepts of GMF modeling at the level of (1) derivation of model equations and (2) their implementation in the Flow-Py code. Therefore, Flow-Py is an educational, innovative GMF simulation tool that can be applied for basic simulations but also for more sophisticated and custom applications such as identifying forests with a protective function or quantifying effects of forests on snow avalanches, rockfall, landslides, and debris flows. </jats:p

Flow-Py v1.0: A customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows

Abstract. Models and simulation tools for gravitational mass flows (GMF) such as snow avalanches, rockfall, landslides and debris flows are important for research, education and practice. In addition to basic simulations and classic applications (e.g., hazard zone mapping), the importance and adaptability of GMF simulation tools for new and advanced applications (e.g., automatic classification of terrain susceptible for GMF initiation or identification of forests with a protective function) are currently driving model developments. In principle, two types of modeling approaches exist: process-based physically motivated and data-based empirically motivated models. The choice for one or the other modeling approach depends on the addressed question, the availability of input data, the required accuracy of the simulation output, and the applied spatial scale. Here we present the computationally inexpensive open-source GMF simulation tool Flow-Py. Flow-Py’s model equations are implemented via the Python computer language and based on geometrical relations motivated by the classical data-based runout angle concepts and path routing in three-dimensional terrain. That is, Flow-Py employs a data-based modeling approach to identify process areas and corresponding intensities of GMFs by combining models for routing and stopping, which depend on local terrain and prior movement. The only required input data are a digital elevation model, the positions of starting zones and a minimum of four model parameters. In addition to the major advantage that the open-source code is freely available for further model development, we illustrate and discuss Flow-Py’s key advancements and simulation performance by means of three computational experiments: 1. Implementation and validation: We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topograhies. 2. Performance: Flow-Py’s performance and low computation time is demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. 3. Modularity and expandability: The modular and adaptive Flow-Py development environment allows to access spatial information easily and consistently, which enables, e.g., back-tracking of GMF paths that interact with obstacles to their starting zones. The aim of this contribution is to enable the reader to reproduce and understand the basic concepts of GMF modeling at the level of 1) derivation of model equations, and 2) their implementation in the Flow-Py code. Therefore, Flow-Py is an educational, innovative GMF simulation tool that can be applied for basic simulations but also for more sophisticated and custom applications such as identifying forests with a protective function or quantifying effects of forests on snow avalanches, rockfall, landslides and debris flows. </jats:p